Systems, methods, and computer readable media for predictive analytics and change detection from remotely sensed imagery

ABSTRACT

Systems and methods are provided for automatically detecting a change in a feature. For example, a system includes a memory and a processor configured to analyze a change associated with a feature over a period of time using a plurality of remotely sensed time series images. Upon execution, the system would receive a plurality of remotely sensed time series images, extract a feature from the plurality of remotely sensed time series images, generate at least two time series feature vectors based on the feature, where the at least two time series feature vectors correspond to the feature at two different times, create a neural network model configured to predict a change in the feature at a specified time, and determine, using the neural network model, the change in the feature at a specified time based on a change between the at least two time series feature vectors.

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.62/767,257, filed Nov. 14, 2018, which is incorporated by referenceherein in its entirety. This application is also related to thefollowing application, which is incorporated by reference in itsentirety: U.S. patent application Ser. No. 15/253,488, titled “Systemsand methods for analyzing remote sensing imagery,” which was filed onAug. 31, 2016.

TECHNICAL FIELD

Embodiments of the present disclosure relate to the field of changedetection, predictive analytics, and predictive maintenance fromremotely sensed imagery.

BACKGROUND OF THE INVENTION

Real world objects change over time. For example, a roof of a housedegrades over time due to natural events, such as rain, wind, andsunlight. As another example, a tree can grow or die over time.Information related to these changes can be useful. For example, aninsurance company can assess a risk level for a house depending on thecondition of the roof of the house and/or the condition of thesurrounding trees. Although these changes can be detected and assessedmanually, the manual process is costly, time-consuming, and error-prone.

Therefore, there is a need in the art for improved systems and methodsfor detecting and analyzing changes in real world objects.

SUMMARY

This invention includes systems and methods that allow automaticdetection of changes of an object over time. Non-limiting examples of anobject include a house, a driveway, a car, a farmland, a constructionsite, a neighborhood/community, etc. These changes may occur on theappearance of an object. For example, a displacement of a shingle on aroof. These changes could be dimensional changes such as a growth of atree over time, or changes in the water level of a body of water.

Disclosed subject matter includes, in one aspect, a system forautomatically detecting a change of an object feature over time. Thesystem includes a memory that stores a computational module configuredto analyze a change of an object feature over a period of time. Thesystem also includes a processor that is coupled to the memory andconfigured to execute the stored computational module. Upon execution,the system would detect a change of an object feature over time using aneural network model.

In an exemplary scenario, upon execution, the system would receive aplurality of remotely sensed time series images over a period of time.In some instances, these images may be transmitted from a remote imagecapturing device such as a satellite camera. Subsequently, the systemwould extract at least one feature from the plurality of remotely sensedtime series images. For example, shingles placement on a roof that isunder construction. The system then generates a time series featurevector corresponding to the feature captured in an image at a particulartime. To identify a change, the system would generate at least two timeseries feature vectors among the plurality of remotely sensed timeseries images. The system then creates a neural network model to predicta change in the feature at a specified time based on the featurevectors. The specified time can be now or at certain time in the future.To determine the change, the system would identify the change (or changepattern) associated with the time series feature vectors and use thechange to output a prediction.

Disclosed subject matter includes, in another aspect, a process fordetecting a change of a feature through a plurality of remotely sensedtime series images. The process includes receiving a plurality ofremotely sensed time series image. The process then proceeds to extracta feature from the plurality of remotely sensed time series images. Theprocess then proceeds to generate at least two time series featurevectors associated with the feature extracted from the plurality ofremotely sensed time series images at two different time period. Theprocess then proceeds to create a neural network model configured topredict a change (or a change pattern) in the feature at a specifictime. The process then proceeds to determine the change of the featurebased on the change (or the change pattern) between the time seriesfeature vectors.

Disclosed subject matter includes, in yet another aspect, anon-transitory computer readable medium having executable instructionsoperable to cause a system to receive a plurality of remotely sensedtime series images. The instructions are further operable to cause thesystem to extract a feature from the plurality of remotely sensed timeseries images. The instructions are further operable to cause the systemto generate at least two time series feature vectors based on thefeature. The generated time series feature vectors correspond to thestate of the feature at different times. The instructions are furtheroperable to cause the system to create a neural network model configuredto predict a change in the feature at a specified time. The instructionsare further operable to cause the system to determine, using the neuralnetwork model, the change in the feature at a specified time based on achange between the at least two time series feature vectors.

Before explaining example embodiments consistent with the presentdisclosure in detail, it is to be understood that the disclosure is notlimited in its application to the details of constructions and to thearrangements set forth in the following description or illustrated inthe drawings. The disclosure is capable of embodiments in addition tothose described and is capable of being practiced and carried out invarious ways. Also, it is to be understood that the phraseology andterminology employed herein, as well as in the abstract, are for thepurpose of description and should not be regarded as limiting.

These and other capabilities of embodiments of the disclosed subjectmatter will be more fully understood after a review of the followingfigures, detailed description, and claims.

It is to be understood that both the foregoing general description andthe following detailed description are explanatory only and are notrestrictive of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects, features, and advantages of the disclosed subjectmatter can be more fully appreciated with reference to the followingdetailed description of the disclosed subject matter when considered inconnection with the following drawings, in which like reference numeralsidentify like elements.

FIG. 1 illustrates a method for detecting and analyzing a change in anobject feature through a plurality of remotely sensed time series imagesin accordance with some embodiments of the present disclosure.

FIG. 2 illustrates a process for determining a change in a feature at aspecified time in accordance with some embodiments of the presentdisclosure.

FIG. 3 illustrates a system for detecting and analyzing a change in anobject feature through a plurality of remotely sensed time series imagesin accordance with some embodiments of the present disclosure.

FIG. 4 illustrates a block diagram of a server in accordance with someembodiments of the present disclosure.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forthregarding the systems and methods of the disclosed subject matter andthe environment in which such systems and methods may operate, etc., inorder to provide a thorough understanding of the disclosed subjectmatter. It will be apparent to one skilled in the art, however, that thedisclosed subject matter may be practiced without such specific details,and that certain features, which are well known in the art, are notdescribed in detail in order to avoid complication of the disclosedsubject matter. In addition, it will be understood that the examplesprovided below are exemplary, and that it is contemplated that there areother systems and methods that are within the scope of the disclosedsubject matter.

Disclosed systems, methods, and computer readable media can be used todetect changes in time series data, provide predictive analytics basedon the detected changes, and perform predictive maintenance using timeseries data. For instance, the disclosed system can detect progressivematerial change over time to determine the desirable schedule formaintenance. In some embodiments, predictive maintenance means that ameasure for means of preventing total breakdown of an item (for examplethe roof of a house) can be avoided by sending a craftsman/handyman tofix the gradual damage once the damage (which is over a predefinedthreshold) was assessed by a method incorporating predictive analytics.Hence the damage can be kept under more control, causes less cost thanin the catastrophic case. Further, the chances to send a professionalinspector to the site without a serious cause (hence causing unnecessarycosts) can be kept at a minimum. In some embodiments, a predictive modelmeans a state (for example a roof condition) that can be extrapolatedseveral steps into the future and thus estimated how it will evolve overtime by taking into account the state history. In some embodiments, thetime series data can include remotely sensed imagery, which can beacquired by using an image acquisition device (e.g., the imageacquisition device described in FIG. 3). The changes that can bedetected can include changes in a built environment, vegetation (e.g.,changes in landscape), topology (e.g. changes due to natural disasters,such as a landslide, tornado, volcano, earthquake, etc.), and any othersuitable changes that can be detected. In some embodiments, the trainingset of the disclosed system can differentiate important and lessimportant visual changes. For example, the disclosed system candetermine that certain features contained within the image are due toseasonal changes while others are due to hazardous events such as awildfire.

According to some embodiments, deep learning of time series data can beapplied to infer a single state and/or a sequence of states of a sceneor an object within the time series data. In some embodiments, deeplearning of time series data can be applied to perform predictiveanalysis of the future evolution of such states. In some embodiments, acomputer neural network model, such as a deep neural network (DNN), canbe used for deep learning. In some embodiments, a scene can include oneor more objects (e.g., an artificial object, such as a house, abuilding, a roof, a pool, a driveway, a shed, or any other type ofartificial object, and/or a natural object, such as a tree, a body ofwater, vegetation, or any other type of natural object). For example, inremotely sensed imagery of property parcels, the images can include ahouse, a roof (which can be part of the house), a shed, a pool, trees,rocks, and/or any other similar items.

According to some embodiments, images of certain locations can be takenon a regular basis (e.g., every month, every six months, every year, orany other suitable regular interval) and/or on an irregular basis (e.g.,images taken as needed). For example, in the case of remotely sensedimagery of property parcels, images of the property parcels can be takenon the first day of each month. These images can be used to create deeplearning models, such as neural network models, to provide systems,methods, and computer readable media for change detection, predictiveanalytics, and predictive maintenance.

According to some embodiments, the use of deep learning can becategorized into at least three types: many-to-one inference;many-to-many inference; and many-to-many prediction. In the many-to-oneinference type, the current state can be inferred from a sequence oftime steps taken up until the present time. For example, given remotelysensed images of a house over the course of twelve months, where oneimage was acquired each month, one can infer on the current condition ofthe roof by presenting the sequence of images to the DNN. In themany-to-many inference type, the state at each time step up until thepresent time can be inferred. For example, given remotely sensed imagesof a house over the course of twelve months, where one image wasacquired each month, one can infer on the condition of the roof of thehouse in each month. In the many-to-many prediction type, the evolutionof a state or multiple states in the future can be predicted. Forexample, given remotely sensed images of a house over the course oftwelve months, where one image was acquired each month, one can predictthe state or states in the future months to come with increasinguncertainty. In some embodiment, the uncertainty is based on aconfidence interval associated with one or more sampled instances. Forexample, in the case of roof condition, the confidence intervalassociated with a near term prediction using recently obtained imagesmay be higher than the confidence interval associated with a distantfuture prediction using recently obtained images. In some embodiments, arecurrent neural network (RNN) model can receive, as an input, thesequence of images up until the present time. The model can then runwithout input from one or several time steps in order to generate aprediction for one or multiple time steps. For example, if a time seriesspans over 6 months, at the fourth month the prediction model can runwithout inputs from the first and second month.

According to some embodiments, instead of estimating a single timeinstance target prediction (following a many-to-one paradigm), an RNNmodel can be used for sequence-to-sequence (many-to-many) mapping. Inthese embodiments, the RNN can predict the development and/or behaviorof an estimated target value over time. This can be achieved either byapplying the RNN to a running window or to an indefinitely longsequence. When applying the RNN to the running window, in someembodiments, the RNN resets after a predefined amount of samples havebeen obtained. In some embodiments, RNN can also be trained on a streamof data (which, in some embodiments, can be infinitely in length) by notresetting after a predefined amount of samples.

According to some embodiments, predictive analytics refer to a machinelearning domain in which models trained on time series of data can beused to predict the future behavior of a real-world system. In someembodiments, a time series can be generated using a convolutional neuralnetwork (CNN) in the context of remotely sensed imagery. The CNN canserve as a feature extractor. For example, a CNN can be applied to atime series of successive remotely sensed images of a physical location(e.g., a house or a property parcel) to extract a feature from thephysical location and generate a feature vector corresponding to thefeature. For example, the CNN can extract the roof as a feature fromeach of the successive remotely sensed images of a house. In someembodiments, the resulting feature vectors and the corresponding timesteps can be used to train an RNN. The RNN can be used to handle dynamictemporal behavior, where the target value for training can be taken fromseveral steps into the future of the time series. For example, the RNNcan receive time-series of feature vectors extracted from the image ofthe roof of a house over a period of time. Based on this time-series offeature vectors, the state of the roof at a particular time (e.g., pastor present) can be inferred and/or the state of the roof in the futurecan be predicted. In some embodiments, the state of the roof can beassigned a score (e.g., a scale of 1 to 5, where 1 represents the statebeing very bad and 5 represents the state being excellent). For example,for a time-series [t₀, t₁, t₂, t₃, t₄, t₅, t₆, t₇, t₈] (where the timet₀ to t₈ represent regular intervals at which the images were taken inthe past), the feature vectors could correspond to scores [5, 5, 4, 1,5, 5, 4, 4, 3]. In some embodiments, each time stamp is associated witha score. In some embodiments, each time stamp is associated with othersuitable indicators, classes, and/or distributions. Based on thesescores, the state of the roof of the house can be inferred for each ofthe time instances in the time-series. It can also be inferred that attime t₄, the roof likely experienced a sudden damage possibly due to adisaster (e.g., a fire or a falling tree) and that at time t₅, a newroof likely had been installed. Moreover, the state of the roof at thepresent (which is time t₈ in this example) can be predicted to be 3based on the trend. The future state(s) of the roof can be predictedsimilarly.

According to some embodiments, the resulting estimation or predictioncan be interpreted as the probability of a certain state occurring Nsteps in the future. In some embodiments, the CNN and the RNN can betrained disjointly in two successive steps. In some embodiments, weightsof CNN models that have been pre-trained on a certain problem domain canbe used. For example, weights of CNN models that have been pre-trainedon remotely sensed imagery of house roofs can be used. The weights arethe parameter that models the connection between the neurons of the CNN.In some embodiments, the RNN can be built using one to severalsuccessive layers. In some instances, a deep RNN can span over severalrecurrent layers in sequence.

FIG. 1 illustrates a method 100 for detecting and analyzing a change ina feature in remotely sensed time series images according to certainembodiments. In some embodiments, the method utilizes a DNN model forpredictive analytics. In some embodiments, the DNN model can include twoparts: a CNN 110 and an RNN 130. In some embodiments, the CNN 110 istrained independently from the RNN 130.

According to certain embodiments, the CNN contains a Deep Learningalgorithm which can take in an image, assign importance (learnableweights and biases) to various aspects/objects in the image and be ableto differentiate one form the other. The CNN is trained to learn theparameters and transformation values associated with an image. In aniterative process, CNN can be trained to identify and map spatialparameters to the space of parcel boundaries, and thus identify theparcel. A training example for the CNN is disclosed in U.S. patentapplication Ser. No. 15/253,488, which is incorporated by reference inits entirety.

According to some embodiments, the RNN includes multiple copies of thesame network, each passing a message to a successor. Like the CNN, theRNN can accept an input vector x, and gives an output vector y. However,unlike a CNN, the output vector's contents are influenced not only bythe input provided by the user, but also by the entire history of inputsthat the user has provided in the past. According to some embodiments,the RNN has an internal state that gets updated every time a new inputis received.

According to some embodiments, the CNN 110 serves as a featureextractor. The CNN 110 can receive a set of time series images 101_1,101_2, . . . , 101_n that respectively correspond to the time series[t₀, t₁, . . . , t_(n)] (where n can be equal to or greater than 1, andwhen n is equal to 1, the time series set would include only twoelements: t₀ and t₁). In some embodiments, the time series images 101_1,101_2, . . . , 101_n can be remotely sensed images (e.g., aerialimages).

According to some embodiments, for each of the time-series images 101_1,101_2, . . . , 101_n, the CNN 110 can extract a feature vector x,resulting in a time series of feature vectors x₀ 121_1, x₁ 121_2, . . ., x_(n) 121_n, which respectively correspond to the time-series images101_1, 101_2, . . . , 101_n. In some embodiments, the CNN can identifyan object based on the object's primitive features such as edges,shapes, and/or any combination thereof. For example, the CNN mayrecognize a house's roof based on features such as the roof's edges andshapes.

According to some embodiments, the RNN 130 can receive the time seriesof feature vectors x₀ 121_1, x₁ 121_2, . . . , x_(n) 121_n as inputsfrom CNN 110. In some embodiments, the RNN 130 can make inference on asingle prediction y 150 and/or a time-series of predictions [y₀ . . .y_(k)] (not shown in the figure) (where k can be equal to or greaterthan 1) that can depend on the problem domain.

FIG. 2 illustrates a process for determining a change in a feature at aspecified time in accordance with some embodiments of the presentdisclosure. In some embodiments, the process 200 can be modified by, forexample, having steps combined, divided, rearranged, changed, added,and/or removed.

According to some embodiments, at step 202, a server receives aplurality of remotely sensed time series images. In some embodiments,the plurality of remotely sensed time series images are captured atregular intervals. In some embodiments, the plurality of remotely sensedtime series images are captured at irregular intervals. The process 200then proceeds to step 204.

At step 204, the server extracts one or more features from the pluralityof remotely sensed time series images received at step 202. In someembodiments, the feature can be extracted by a CNN. The feature can be apermanent feature, a temporary feature, or a changing feature. Forexample, in a roofing context, the permanent feature is a roof'sshingles; the temporary feature is observable changes due to sun lightor weather; and the changing feature is shingle conditions, missingshingles, streaks, and/or spots. In some embodiments, the feature is atleast one of a property condition, a neighborhood condition, a builtenvironment, a vegetation state, a topology, a roof, a building, a tree,or a body of water. In some embodiments, the feature is defined by anaggregated state derived from the sub-states of partial features (orsub-components). For example, the feature can be an overall neighborhoodstate, which can be an aggregate state of a plurality of sub-statescorresponding to sub-components (e.g., the houses within theneighborhood, the roads within the neighborhood, the bodies of waterwithin the neighborhood, and any other sub-features that can part of theneighborhood). The process 200 then proceeds to step 206.

At step 206, the server generates one or more time series featurevectors based on the feature, each of which corresponds to the featurefrom the each of the plurality of time series images extracted at step204. In some embodiments, the plurality of time series feature vectorscan be generated by the CNN. In some embodiments, the server furtherdetermines a plurality of time series scores, each of which correspondsto the feature from the each of the plurality of time series images. Theprocess 200 then proceeds to step 208.

At step 208, the server creates a neural network model configured topredict a change in the feature based on the plurality of time seriesfeature vectors generated in step 206 In some embodiments, insignificantchanges can be discarded when the neural network model is created.Insignificant changes may include changes that are not important to thestate of the feature. For example, a shadow on a roof is not significantto the state of the roof. The neural network model can be trained toidentify these insignificant changes. For example, the training datasetcan contain two images of a roof, one with shadow and one withoutshadow. The system then applies the same ground-truth mapping value isto both images irrespective of the shadow effect, hence, forcing theneural network to become invariant to shadows when trained with thisdata. In some embodiments, the neural network model can be composed of aneural network that is at least one of an RNN or a CNN. The process 200then proceeds to step 210.

At step 210, the server determines, using the neural network model, achange in the feature at a specified time based on a changebetween/among the time series feature vectors. In some embodiments, thespecified time is the present. In some embodiments, the specified timeis in the future. In some embodiments, the change in the feature can bepredicted based on a plurality of time series scores.

FIG. 3 illustrates a system 300 for detecting and analyzing a change ina feature in remotely sensed time series images. The system 300 caninclude a server 304, at least one client device 301 (e.g., clientdevices 301-1, 301-2, . . . , 301-N), an imagery acquisition device 303,a local storage medium 305, and a remote storage medium 306. Allcomponents in the system 300 can be coupled directly or indirectly to acommunication network 302. The components described in the system 300can be further broken down into more than one component and/or combinedtogether in any suitable arrangement. Further, one or more componentscan be rearranged, changed, added, and/or removed. For example, in someembodiments, the system 300 can obtain the data from third-partyvendors. In other embodiments, the system 300 can directly acquire datathrough the imagery acquisition device 303.

Each client device 301 can communicate with the server 304 to send datato, and receive data from, the imagery analysis server 304 via thecommunication network 302. Each client device 301 can be directly orindirectly coupled to the server 304. Additionally, each client device301 can be connected to the server 304 via any other suitable device(s),communication network, or a combination thereof. A client device 301 caninclude, for example, a desktop computer, a mobile computer, a tabletcomputer, a cellular device, a smartphone, a television, or anycomputing system that is capable of performing the computation processesdescribed above.

The server 304 is configured to receive imagery data from the imageryacquisition device 303. The imagery analysis server 304 can extract,analyze, and/or label structural and/or geospatial information of thereceived imagery data based on the techniques disclosed in this presentdisclosure. In some embodiments, a classifier can be trained and/ormaintained in the server 304. The server 304 is shown as a singleserver; however, the server 304 can include more than one server. Forexample, in some embodiments, the server 304 can include multiplemodular and scalable servers and/or other suitable computing resources.The server 304 can support elastic computing, which can dynamicallyadapt computing resources based on demand. The server 304 can bedeployed locally and/or remotely in a third-party cloud-based computingenvironment. In some embodiments, within the server 304 or any othersuitable component of system 300, a device or a tool—including thosedescribed in the present disclosure—can be implemented as softwareand/or hardware.

The imagery acquisition device 303 is configured to provide the server304 with imagery data. In some embodiments, the imagery acquisitiondevice 303 can acquire satellite imagery, aerial imagery, radar, sonar,LIDAR, seismography, or any other suitable mode or combination of modesof sensory information. In some embodiments, the system 300 does notinclude the imagery acquisition device 303 and can obtain imagery datafrom third-party vendors. In some embodiments, the imagery acquisitiondevice can be part of a GIS. The system 300 includes two storage media:the local storage medium 305 and the remote storage medium 306.

The local storage medium 305 can be located in the same physicallocation as the server 304, and the remote storage medium 306 can belocated at a remote location or any other suitable location orcombination of locations. In some embodiments, the system 300 includesmore than one local storage medium, more than one remote storage medium,and/or any suitable combination thereof. In some embodiments, the system300 may only include the local storage medium 305 or only include theremote storage medium 306.

The system 300 can also include one or more relational databases, whichinclude scalable read replicas to support dynamic usage. The one or morerelational databases can be located in the local storage medium 305, theremote storage medium 306, the server 304, and/or any other suitablecomponents, combinations of components, or locations of the system 300.

The communication network 302 can include the Internet, a cellularnetwork, a telephone network, a computer network, a packet switchingnetwork, a line switching network, a local area network (LAN), a widearea network (WAN), a global area network, or any number of privatenetworks currently referred to as an Intranet, and/or any other networkor combination of networks that can accommodate data communication. Suchnetworks may be implemented with any number of hardware and softwarecomponents, transmission media and/or network protocols. In someembodiments, the communication network 302 can be an encrypted network.While the system 300 shows the communication network 302 as a singlenetwork, the communication network 302 can also include multipleinterconnected networks described above.

FIG. 4 illustrates a block diagram of the server 304 in accordance withsome embodiments of the present disclosure. The server 304 includes aprocessor 402, a memory 404, and a module 406. The server 304 mayinclude additional modules, fewer modules, or any other suitablecombination of modules that perform any suitable operation orcombination of operations.

The processor 402 is configured to implement the functionality describedherein using computer executable instructions stored in temporary and/orpermanent non-transitory memory. The processor can be a general purposeprocessor and/or can also be implemented using an application specificintegrated circuit (ASIC), programmable logic array (PLA), fieldprogrammable gate array (FPGA), and/or any other integrated circuit.

The processor 402 can execute an operating system that can be anysuitable operating system (OS), including a typical operating systemsuch as Windows, Windows XP, Windows 7, Windows 8, Windows Mobile,Windows Phone, Windows RT, Mac OS X, Linux, VXWorks, Android, BlackberryOS, iOS, Symbian, or other OS.

The module 406 and/or other modules in the server 304 can be configuredto cause the processor 402 or the server 304 to perform variousfunctions described herein. These functions can include the functionsrelated to the CNN 110 and RNN 130, as described in connection with FIG.1 above. In some embodiments, within the module 406 or any othersuitable component of the server 304, a device or a tool can beimplemented as software and/or hardware.

In some embodiments, the module 406 can be implemented in software usingthe memory 404. The memory 404 can be a non-transitory computer readablemedium, flash memory, a magnetic disk drive, an optical drive, aprogrammable read-only memory (PROM), a read-only memory (ROM), or anyother memory or combination of memories.

It is contemplated that systems, devices, methods, and processes of thedisclosure invention encompass variations and adaptations developedusing information from the embodiments described herein. Adaptationand/or modification of the systems, devices, methods, and processesdescribed herein may be performed by those of ordinary skill in therelevant art.

Throughout the description, where articles, devices, and systems aredescribed as having, including, or comprising specific components, orwhere processes and methods are described as having, including, orcomprising specific steps, it is contemplated that, additionally, thereare articles, devices, and systems of the present disclosure thatconsist essentially of, or consist of, the recited components, and thatthere are processes and methods according to the present disclosure thatconsist essentially of, or consist of, the recited processing steps.

It should be understood that the order of steps or order for performingcertain action is immaterial so long as the disclosure remains operable.Moreover, two or more steps or actions may be conducted simultaneously.The mention herein of any publication, for example, in the Backgroundsection, is not an admission that the publication serves as prior artwith respect to any of the claims presented herein. The Backgroundsection is presented for purposes of clarity and is not meant as adescription of prior art with respect to any claim.

It is to be understood that the disclosed subject matter is not limitedin its application to the details of construction and to thearrangements of the components set forth above or illustrated in thedrawings. The disclosed subject matter is capable of other embodimentsand of being practiced and carried out in various ways. Also, it is tobe understood that the phraseology and terminology employed herein arefor the purpose of description and should not be regarded as limiting.As such, those skilled in the art will appreciate that the conception,upon which this disclosure is based, may readily be utilized as a basisfor the designing of other structures, methods, and systems for carryingout the several purposes of the disclosed subject matter.

What is claimed is:
 1. A method of detecting a change in a feature inremotely sensed time series images, the method comprising: receiving, ata server, a plurality of remotely sensed time series images; extracting,at the server, a feature from the plurality of remotely sensed timeseries images; generating, at the server, at least two time seriesfeature vectors based on the feature, wherein the at least two timeseries feature vectors correspond to the feature at two different times;creating, at the server, a neural network model configured to predict achange in the feature at a specified time; and determining, at theserver using the neural network model, the change in the feature at thespecified time based on a change between the at least two time seriesfeature vectors, wherein the neural network model receives as input theat least two time series feature vectors.
 2. The method of claim 1,wherein the feature is extracted by a convolutional neural network, andwherein the at least two time series feature vectors are generated bythe convolutional neural network.
 3. The method of claim 1, wherein theneural network model is based on a neural network that comprises atleast one of a recurrent neural network or a convolutional neuralnetwork.
 4. The method of claim 1, wherein the plurality of remotelysensed time series images are captured at regular intervals.
 5. Themethod of claim 1, wherein the plurality of remotely sensed time seriesimages are captured at non-regular intervals.
 6. The method of claim 1,wherein the feature is at least one of a property condition, aneighborhood condition, a built environment, a vegetation state, atopology, a roof, a building, a tree, or a body of water.
 7. The methodof claim 1, wherein the feature is defined by an aggregated state basedon one or more sub-states of one or more partial features.
 8. The methodof claim 1 further comprising determining, at the server, a plurality oftime series scores, each of which corresponds to the feature.
 9. Themethod of claim 8, wherein the change in the feature is determined basedon the plurality of time series scores.
 10. The method of claim 8,wherein an insignificant change in the feature is discarded.
 11. Aserver for detecting a change in a feature in remotely sensed timeseries images, the server comprising: a memory that stores a module; anda processor configured to run the module stored in the memory that isconfigured to cause the processor to: receive a plurality of remotelysensed time series images; extract a feature from the plurality ofremotely sensed time series images; generate at least two time seriesfeature vectors based on the feature, wherein the at least two timeseries feature vectors correspond to the feature at two different times;create a neural network model configured to predict a change in thefeature at a specified time; and determine, using the neural networkmodel, the change in the feature at the specified time based on a changebetween the at least two time series feature vectors, wherein the neuralnetwork model receives as input the at least two time series featurevectors.
 12. The server of claim 11, wherein the feature is extracted bya convolutional neural network, and wherein the at least two time seriesfeature vectors are generated by the convolutional neural network. 13.The server of claim 11, wherein the neural network model is based on aneural network that comprises at least one of a recurrent neural networkor a convolutional neural network.
 14. The server of claim 11, whereinthe plurality of remotely sensed time series images is captured atregular intervals.
 15. The server of claim 11, wherein the plurality ofremotely sensed time series images is captured at non-regular intervals.16. The server of claim 11, wherein the feature is at least one of aproperty condition, a neighborhood condition, a built environment, avegetation state, a topology, a roof, a building, a tree, or a body ofwater.
 17. The server of claim 11, wherein the feature is defined by anaggregated state based on one or more sub-states one or more partialfeatures.
 18. The server of claim 11, wherein the module stored in thememory is further configured to cause the processor to determine aplurality of time series scores, each of which corresponds to thefeature.
 19. The server of claim 18, wherein the change in the featureis determined based on the plurality of time series scores.
 20. Theserver of claim 18, wherein an insignificant change is discarded.
 21. Anon-transitory computer readable medium storing executable instructionsoperable for detecting a change in a feature in remotely sensed timeseries images to cause a processor to perform operations comprising:receiving a plurality of remotely sensed time series images; extractinga feature from the plurality of remotely sensed time series images;generating at least two time series feature vectors based on thefeature, wherein the at least two time series feature vectors correspondto the feature at two different times; creating a neural network modelconfigured to predict a change in the feature at a specified time; anddetermining, using the neural network model, the change in the featureat a specified time based on a change between the at least two timeseries feature vectors, wherein the neural network model receives asinput the at least two time series feature vectors.